Mapping the energy and diffusion landscapes of membrane proteins at the cell surface using high-density single-molecule imaging and Bayesian inference: application to the multi-scale dynamics of glycine receptors in the neuronal membrane

TL;DR

This paper introduces a method combining high-density single-molecule imaging and Bayesian inference to map the diffusion and energy landscapes of membrane proteins at high resolution, revealing how biochemical interactions influence protein mobility.

Contribution

The study presents a novel analytical framework that separately maps diffusion and energy landscapes of membrane proteins, enabling detailed analysis of their multi-scale dynamics in living cells.

Findings

GlyRs are trapped by shallow energy wells (~3 kBT) at gephyrin scaffolds.

The energy landscape depth varies with receptor-gephyrin interaction properties.

The inferred maps can simulate protein dynamics and physiological fluctuations.

Abstract

Protein mobility is conventionally analyzed in terms of an effective diffusion. Yet, this description often fails to properly distinguish and evaluate the physical parameters (such as the membrane friction) and the biochemical interactions governing the motion. Here, we present a method combining high-density single-molecule imaging and statistical inference to separately map the diffusion and energy landscapes of membrane proteins across the cell surface at ~100 nm resolution (with acquisition of a few minutes). When applying these analytical tools to glycine neurotransmitter receptors (GlyRs) at inhibitory synapses, we find that gephyrin scaffolds act as shallow energy traps (~3 kBT) for GlyRs, with a depth modulated by the biochemical properties of the receptor-gephyrin interaction loop. In turn, the inferred maps can be used to simulate the dynamics of proteins in the membrane, from the level of individual receptors to that of the population, and thereby, to model the stochastic fluctuations of physiological parameters (such as the number of receptors at synapses). Overall, our approach provides a powerful and comprehensive framework with which to analyze biochemical interactions in living cells and to decipher the multi-scale dynamics of biomolecules in complex cellular environments.